Overview

Dataset statistics

Number of variables12
Number of observations613888
Missing cells214752
Missing cells (%)2.9%
Duplicate rows5
Duplicate rows (%)< 0.1%
Total size in memory56.2 MiB
Average record size in memory96.0 B

Variable types

DateTime1
Categorical3
Numeric8

Warnings

Dataset has 5 (< 0.1%) duplicate rows Duplicates
Party_Name has a high cardinality: 147 distinct values High cardinality
QueuedTime is highly correlated with WaitTime and 1 other fieldsHigh correlation
RingTime is highly correlated with WaitTime vs QueuedTimeHigh correlation
WaitTime is highly correlated with QueuedTime and 1 other fieldsHigh correlation
Queue + Ring is highly correlated with QueuedTime and 1 other fieldsHigh correlation
WaitTime vs QueuedTime is highly correlated with RingTimeHigh correlation
Party_Name has 51804 (8.4%) missing values Missing
TalkTime has 36852 (6.0%) missing values Missing
HoldTime has 60255 (9.8%) missing values Missing
WrapTime has 65841 (10.7%) missing values Missing
QueuedTime is highly skewed (γ1 = 48.74867442) Skewed
WrapTime is highly skewed (γ1 = 379.3110619) Skewed
WaitTime is highly skewed (γ1 = 49.86471147) Skewed
Queue + Ring is highly skewed (γ1 = 49.86471147) Skewed
QueuedTime has 35987 (5.9%) zeros Zeros
RingTime has 39115 (6.4%) zeros Zeros
TalkTime has 24028 (3.9%) zeros Zeros
HoldTime has 447578 (72.9%) zeros Zeros
WrapTime has 10994 (1.8%) zeros Zeros
WaitTime vs QueuedTime has 39115 (6.4%) zeros Zeros

Reproduction

Analysis started2021-02-26 15:33:35.625015
Analysis finished2021-02-26 15:34:12.643074
Duration37.02 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

Distinct597403
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Memory size4.7 MiB
Minimum2020-01-01 08:22:41.999998
Maximum2021-02-21 12:24:08.999997
2021-02-26T16:34:12.708062image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:12.844271image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Exit_Reason
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.7 MiB
AgentAnswered
562100 
Abandoned
 
48035
Redirected
 
3753

Length

Max length13
Median length13
Mean length12.66867083
Min length9

Characters and Unicode

Total characters7777145
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAbandoned
2nd rowAbandoned
3rd rowAbandoned
4th rowAbandoned
5th rowAbandoned
ValueCountFrequency (%)
AgentAnswered562100
91.6%
Abandoned48035
 
7.8%
Redirected3753
 
0.6%
2021-02-26T16:34:13.092491image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-26T16:34:13.169936image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
agentanswered562100
91.6%
abandoned48035
 
7.8%
redirected3753
 
0.6%

Most occurring characters

ValueCountFrequency (%)
e1745594
22.4%
n1220270
15.7%
A1172235
15.1%
d665676
 
8.6%
t565853
 
7.3%
r565853
 
7.3%
g562100
 
7.2%
s562100
 
7.2%
w562100
 
7.2%
b48035
 
0.6%
Other values (5)107329
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter6601157
84.9%
Uppercase Letter1175988
 
15.1%

Most frequent character per category

ValueCountFrequency (%)
e1745594
26.4%
n1220270
18.5%
d665676
 
10.1%
t565853
 
8.6%
r565853
 
8.6%
g562100
 
8.5%
s562100
 
8.5%
w562100
 
8.5%
b48035
 
0.7%
a48035
 
0.7%
Other values (3)55541
 
0.8%
ValueCountFrequency (%)
A1172235
99.7%
R3753
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin7777145
100.0%

Most frequent character per script

ValueCountFrequency (%)
e1745594
22.4%
n1220270
15.7%
A1172235
15.1%
d665676
 
8.6%
t565853
 
7.3%
r565853
 
7.3%
g562100
 
7.2%
s562100
 
7.2%
w562100
 
7.2%
b48035
 
0.6%
Other values (5)107329
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII7777145
100.0%

Most frequent character per block

ValueCountFrequency (%)
e1745594
22.4%
n1220270
15.7%
A1172235
15.1%
d665676
 
8.6%
t565853
 
7.3%
r565853
 
7.3%
g562100
 
7.2%
s562100
 
7.2%
w562100
 
7.2%
b48035
 
0.6%
Other values (5)107329
 
1.4%

Party_Name
Categorical

HIGH CARDINALITY
MISSING

Distinct147
Distinct (%)< 0.1%
Missing51804
Missing (%)8.4%
Memory size4.7 MiB
Alex Dillon
 
16744
Daniel Schirmer
 
13918
Dave Lee Wincek
 
13590
John Gene Vura
 
13257
Alex Baltas
 
12996
Other values (142)
491579 

Length

Max length17
Median length16
Mean length15.37884017
Min length8

Characters and Unicode

Total characters8644200
Distinct characters51
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowKyle Younglas
2nd rowLuis Torres
3rd rowDaniel Schirmer
4th rowMike Bilfield
5th rowChris Smucny
ValueCountFrequency (%)
Alex Dillon 16744
 
2.7%
Daniel Schirmer 13918
 
2.3%
Dave Lee Wincek13590
 
2.2%
John Gene Vura13257
 
2.2%
Alex Baltas 12996
 
2.1%
Timmy Moran 12857
 
2.1%
Butch Herten 12424
 
2.0%
Mike Pascaru 12390
 
2.0%
Dustin Pollock 11732
 
1.9%
Dan Terbrack 11386
 
1.9%
Other values (137)430790
70.2%
(Missing)51804
 
8.4%
2021-02-26T16:34:13.402845image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
alex58681
 
5.1%
mike46134
 
4.0%
john26111
 
2.3%
matt22635
 
2.0%
dan22432
 
1.9%
dillon20301
 
1.8%
joe19642
 
1.7%
connor18112
 
1.6%
daniel16419
 
1.4%
schirmer16418
 
1.4%
Other values (146)884130
76.8%

Most occurring characters

ValueCountFrequency (%)
2222207
25.7%
e724093
 
8.4%
a580071
 
6.7%
n516805
 
6.0%
r445569
 
5.2%
l403147
 
4.7%
o371993
 
4.3%
i370230
 
4.3%
t258391
 
3.0%
c191036
 
2.2%
Other values (41)2560658
29.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter5249661
60.7%
Space Separator2222207
25.7%
Uppercase Letter1171922
 
13.6%
Other Punctuation410
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
e724093
13.8%
a580071
11.0%
n516805
9.8%
r445569
 
8.5%
l403147
 
7.7%
o371993
 
7.1%
i370230
 
7.1%
t258391
 
4.9%
c191036
 
3.6%
h190793
 
3.6%
Other values (15)1197533
22.8%
ValueCountFrequency (%)
M159310
13.6%
S105566
9.0%
D104942
9.0%
B101363
 
8.6%
A95994
 
8.2%
C93139
 
7.9%
J80590
 
6.9%
T65411
 
5.6%
K65282
 
5.6%
L40930
 
3.5%
Other values (14)259395
22.1%
ValueCountFrequency (%)
2222207
100.0%
ValueCountFrequency (%)
*410
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6421583
74.3%
Common2222617
 
25.7%

Most frequent character per script

ValueCountFrequency (%)
e724093
 
11.3%
a580071
 
9.0%
n516805
 
8.0%
r445569
 
6.9%
l403147
 
6.3%
o371993
 
5.8%
i370230
 
5.8%
t258391
 
4.0%
c191036
 
3.0%
h190793
 
3.0%
Other values (39)2369455
36.9%
ValueCountFrequency (%)
2222207
> 99.9%
*410
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII8644200
100.0%

Most frequent character per block

ValueCountFrequency (%)
2222207
25.7%
e724093
 
8.4%
a580071
 
6.7%
n516805
 
6.0%
r445569
 
5.2%
l403147
 
4.7%
o371993
 
4.3%
i370230
 
4.3%
t258391
 
3.0%
c191036
 
2.2%
Other values (41)2560658
29.6%

channel
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.7 MiB
SEO
455212 
PPC
158676 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1841664
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSEO
2nd rowSEO
3rd rowSEO
4th rowSEO
5th rowSEO
ValueCountFrequency (%)
SEO455212
74.2%
PPC158676
 
25.8%
2021-02-26T16:34:13.602612image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-26T16:34:13.668743image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
seo455212
74.2%
ppc158676
 
25.8%

Most occurring characters

ValueCountFrequency (%)
S455212
24.7%
E455212
24.7%
O455212
24.7%
P317352
17.2%
C158676
 
8.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1841664
100.0%

Most frequent character per category

ValueCountFrequency (%)
S455212
24.7%
E455212
24.7%
O455212
24.7%
P317352
17.2%
C158676
 
8.6%

Most occurring scripts

ValueCountFrequency (%)
Latin1841664
100.0%

Most frequent character per script

ValueCountFrequency (%)
S455212
24.7%
E455212
24.7%
O455212
24.7%
P317352
17.2%
C158676
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1841664
100.0%

Most frequent character per block

ValueCountFrequency (%)
S455212
24.7%
E455212
24.7%
O455212
24.7%
P317352
17.2%
C158676
 
8.6%

QueuedTime
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct1141
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.05805782
Minimum0
Maximum14829
Zeros35987
Zeros (%)5.9%
Memory size4.7 MiB
2021-02-26T16:34:13.757891image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median4
Q314
95-th percentile100
Maximum14829
Range14829
Interquartile range (IQR)11

Descriptive statistics

Standard deviation76.51917291
Coefficient of variation (CV)3.468989588
Kurtosis6653.688143
Mean22.05805782
Median Absolute Deviation (MAD)2
Skewness48.74867442
Sum13541177
Variance5855.183823
MonotocityNot monotonic
2021-02-26T16:34:13.890050image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4126744
20.6%
377178
12.6%
258122
 
9.5%
537836
 
6.2%
136945
 
6.0%
035987
 
5.9%
613221
 
2.2%
711990
 
2.0%
911693
 
1.9%
811438
 
1.9%
Other values (1131)192734
31.4%
ValueCountFrequency (%)
035987
 
5.9%
136945
 
6.0%
258122
9.5%
377178
12.6%
4126744
20.6%
ValueCountFrequency (%)
148291
< 0.1%
146891
< 0.1%
124581
< 0.1%
87181
< 0.1%
62961
< 0.1%

RingTime
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.545757532
Minimum0
Maximum48
Zeros39115
Zeros (%)6.4%
Memory size4.7 MiB
2021-02-26T16:34:14.024611image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median7
Q310
95-th percentile16
Maximum48
Range48
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.599425179
Coefficient of variation (CV)0.6095378972
Kurtosis0.2803712952
Mean7.545757532
Median Absolute Deviation (MAD)3
Skewness0.6248949212
Sum4632250
Variance21.15471198
MonotocityNot monotonic
2021-02-26T16:34:14.143871image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
659588
9.7%
557758
 
9.4%
753863
 
8.8%
453126
 
8.7%
851740
 
8.4%
945621
 
7.4%
039115
 
6.4%
1038231
 
6.2%
337628
 
6.1%
1131163
 
5.1%
Other values (23)146055
23.8%
ValueCountFrequency (%)
039115
6.4%
19525
 
1.6%
223890
3.9%
337628
6.1%
453126
8.7%
ValueCountFrequency (%)
481
< 0.1%
431
< 0.1%
341
< 0.1%
292
< 0.1%
282
< 0.1%

TalkTime
Real number (ℝ≥0)

MISSING
ZEROS

Distinct1760
Distinct (%)0.3%
Missing36852
Missing (%)6.0%
Infinite0
Infinite (%)0.0%
Mean189.9516183
Minimum0
Maximum5791
Zeros24028
Zeros (%)3.9%
Memory size4.7 MiB
2021-02-26T16:34:14.267595image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q144
median133
Q3253
95-th percentile604
Maximum5791
Range5791
Interquartile range (IQR)209

Descriptive statistics

Standard deviation201.9628504
Coefficient of variation (CV)1.063233113
Kurtosis8.565161138
Mean189.9516183
Median Absolute Deviation (MAD)97
Skewness2.209755917
Sum109608922
Variance40788.99296
MonotocityNot monotonic
2021-02-26T16:34:14.395154image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
024028
 
3.9%
204234
 
0.7%
194125
 
0.7%
184122
 
0.7%
214104
 
0.7%
223962
 
0.6%
173960
 
0.6%
233826
 
0.6%
243800
 
0.6%
163776
 
0.6%
Other values (1750)517099
84.2%
(Missing)36852
 
6.0%
ValueCountFrequency (%)
024028
3.9%
11796
 
0.3%
22001
 
0.3%
31836
 
0.3%
41547
 
0.3%
ValueCountFrequency (%)
57911
< 0.1%
36071
< 0.1%
35471
< 0.1%
32231
< 0.1%
31771
< 0.1%

HoldTime
Real number (ℝ≥0)

MISSING
ZEROS

Distinct636
Distinct (%)0.1%
Missing60255
Missing (%)9.8%
Infinite0
Infinite (%)0.0%
Mean7.585534099
Minimum0
Maximum1789
Zeros447578
Zeros (%)72.9%
Memory size4.7 MiB
2021-02-26T16:34:14.553341image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile51
Maximum1789
Range1789
Interquartile range (IQR)0

Descriptive statistics

Standard deviation34.17326225
Coefficient of variation (CV)4.505056837
Kurtosis123.3907354
Mean7.585534099
Median Absolute Deviation (MAD)0
Skewness8.398149903
Sum4199602
Variance1167.811853
MonotocityNot monotonic
2021-02-26T16:34:14.684455image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0447578
72.9%
121678
 
3.5%
210562
 
1.7%
38276
 
1.3%
45680
 
0.9%
54310
 
0.7%
63576
 
0.6%
72757
 
0.4%
82140
 
0.3%
91452
 
0.2%
Other values (626)45624
 
7.4%
(Missing)60255
 
9.8%
ValueCountFrequency (%)
0447578
72.9%
121678
 
3.5%
210562
 
1.7%
38276
 
1.3%
45680
 
0.9%
ValueCountFrequency (%)
17891
< 0.1%
15981
< 0.1%
12471
< 0.1%
12031
< 0.1%
10791
< 0.1%

WrapTime
Real number (ℝ≥0)

MISSING
SKEWED
ZEROS

Distinct2560
Distinct (%)0.5%
Missing65841
Missing (%)10.7%
Infinite0
Infinite (%)0.0%
Mean151.4322859
Minimum0
Maximum227003
Zeros10994
Zeros (%)1.8%
Memory size4.7 MiB
2021-02-26T16:34:14.825389image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q117
median83
Q3195
95-th percentile525
Maximum227003
Range227003
Interquartile range (IQR)178

Descriptive statistics

Standard deviation383.3986313
Coefficient of variation (CV)2.531815649
Kurtosis223651.6554
Mean151.4322859
Median Absolute Deviation (MAD)74
Skewness379.3110619
Sum82992010
Variance146994.5105
MonotocityNot monotonic
2021-02-26T16:34:15.235451image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
415020
 
2.4%
513983
 
2.3%
313468
 
2.2%
611998
 
2.0%
010994
 
1.8%
710047
 
1.6%
88869
 
1.4%
97592
 
1.2%
106854
 
1.1%
116020
 
1.0%
Other values (2550)443202
72.2%
(Missing)65841
 
10.7%
ValueCountFrequency (%)
010994
1.8%
12022
 
0.3%
25689
 
0.9%
313468
2.2%
415020
2.4%
ValueCountFrequency (%)
2270031
< 0.1%
103861
< 0.1%
98341
< 0.1%
98171
< 0.1%
97261
< 0.1%

WaitTime
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct1147
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.60381535
Minimum0
Maximum14829
Zeros1867
Zeros (%)0.3%
Memory size4.7 MiB
2021-02-26T16:34:15.366699image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q110
median14
Q324
95-th percentile104
Maximum14829
Range14829
Interquartile range (IQR)14

Descriptive statistics

Standard deviation75.84398261
Coefficient of variation (CV)2.56196648
Kurtosis6875.404405
Mean29.60381535
Median Absolute Deviation (MAD)5
Skewness49.86471147
Sum18173427
Variance5752.309698
MonotocityNot monotonic
2021-02-26T16:34:15.495574image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1044687
 
7.3%
942138
 
6.9%
1239387
 
6.4%
1138521
 
6.3%
837921
 
6.2%
1432384
 
5.3%
1330080
 
4.9%
727950
 
4.6%
1624096
 
3.9%
1521703
 
3.5%
Other values (1137)275021
44.8%
ValueCountFrequency (%)
01867
 
0.3%
1843
 
0.1%
21296
 
0.2%
32104
 
0.3%
46128
1.0%
ValueCountFrequency (%)
148291
< 0.1%
146891
< 0.1%
124581
< 0.1%
87181
< 0.1%
62961
< 0.1%

Queue + Ring
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct1147
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.60381535
Minimum0
Maximum14829
Zeros1867
Zeros (%)0.3%
Memory size4.7 MiB
2021-02-26T16:34:15.633923image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q110
median14
Q324
95-th percentile104
Maximum14829
Range14829
Interquartile range (IQR)14

Descriptive statistics

Standard deviation75.84398261
Coefficient of variation (CV)2.56196648
Kurtosis6875.404405
Mean29.60381535
Median Absolute Deviation (MAD)5
Skewness49.86471147
Sum18173427
Variance5752.309698
MonotocityNot monotonic
2021-02-26T16:34:15.762651image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1044687
 
7.3%
942138
 
6.9%
1239387
 
6.4%
1138521
 
6.3%
837921
 
6.2%
1432384
 
5.3%
1330080
 
4.9%
727950
 
4.6%
1624096
 
3.9%
1521703
 
3.5%
Other values (1137)275021
44.8%
ValueCountFrequency (%)
01867
 
0.3%
1843
 
0.1%
21296
 
0.2%
32104
 
0.3%
46128
1.0%
ValueCountFrequency (%)
148291
< 0.1%
146891
< 0.1%
124581
< 0.1%
87181
< 0.1%
62961
< 0.1%

WaitTime vs QueuedTime
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.545757532
Minimum0
Maximum48
Zeros39115
Zeros (%)6.4%
Memory size4.7 MiB
2021-02-26T16:34:15.889225image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median7
Q310
95-th percentile16
Maximum48
Range48
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.599425179
Coefficient of variation (CV)0.6095378972
Kurtosis0.2803712952
Mean7.545757532
Median Absolute Deviation (MAD)3
Skewness0.6248949212
Sum4632250
Variance21.15471198
MonotocityNot monotonic
2021-02-26T16:34:16.005033image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
659588
9.7%
557758
 
9.4%
753863
 
8.8%
453126
 
8.7%
851740
 
8.4%
945621
 
7.4%
039115
 
6.4%
1038231
 
6.2%
337628
 
6.1%
1131163
 
5.1%
Other values (23)146055
23.8%
ValueCountFrequency (%)
039115
6.4%
19525
 
1.6%
223890
3.9%
337628
6.1%
453126
8.7%
ValueCountFrequency (%)
481
< 0.1%
431
< 0.1%
341
< 0.1%
292
< 0.1%
282
< 0.1%

Interactions

2021-02-26T16:33:58.017628image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:33:58.238914image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:33:58.433938image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:33:58.641557image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:33:58.841933image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:33:59.070409image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:33:59.294831image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:33:59.506012image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:33:59.718243image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:33:59.918136image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:00.125951image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:00.342021image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:00.553337image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:00.772199image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:00.969805image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:01.184103image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:01.384925image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:01.586962image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:01.789979image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:02.012753image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:02.238316image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:02.442871image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:02.663684image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:02.879904image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:03.088122image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:03.289758image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:03.496309image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:03.722499image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:03.938499image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:04.141636image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:04.338269image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:04.534131image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:04.735906image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:04.922681image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:05.126802image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:05.330603image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:05.551604image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:05.757574image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:05.952374image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:06.156614image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:06.364691image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:06.598056image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:06.807184image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:07.027120image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:07.241409image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:07.446330image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:07.651437image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:07.857596image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:08.090420image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:08.310463image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:08.536330image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:08.745577image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:08.949353image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:09.159835image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:09.372901image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:09.589990image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-02-26T16:34:16.112734image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-02-26T16:34:16.275676image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-02-26T16:34:16.438616image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-02-26T16:34:16.604882image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-02-26T16:34:16.759742image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-02-26T16:34:10.176322image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-02-26T16:34:10.847991image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-02-26T16:34:11.972685image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-02-26T16:34:12.238499image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Call_StartExit_ReasonParty_NamechannelQueuedTimeRingTimeTalkTimeHoldTimeWrapTimeWaitTimeQueue + RingWaitTime vs QueuedTime
02020-01-01 08:22:41.999998AbandonedNaNSEO110NaNNaNNaN11110
12020-01-01 08:54:11.999998AbandonedNaNSEO1150NaNNaNNaN1151150
22020-01-01 10:41:51.999997AbandonedNaNSEO250NaNNaNNaN25250
32020-01-01 10:47:42.999996AbandonedNaNSEO600NaNNaNNaN60600
42020-01-01 11:06:46.000000AbandonedNaNSEO200NaNNaNNaN20200
52020-01-01 11:25:49.000002AbandonedNaNSEO70NaNNaNNaN770
62020-01-01 15:08:38.000002AbandonedNaNSEO230NaNNaNNaN23230
72020-01-01 16:38:39.000005AbandonedNaNSEO1190NaNNaNNaN1191190
82020-01-01 16:53:30.999999AbandonedNaNSEO120NaNNaNNaN12120
92020-01-01 16:58:58.999999AbandonedNaNSEO200NaNNaNNaN20200

Last rows

Call_StartExit_ReasonParty_NamechannelQueuedTimeRingTimeTalkTimeHoldTimeWrapTimeWaitTimeQueue + RingWaitTime vs QueuedTime
6138782021-02-21 12:07:37.000004AgentAnsweredMatthew DuffSEO1111210.00.00.0222211
6138792021-02-21 12:08:27.000004AgentAnsweredJunior FetchetSEO88133.00.02.016168
6138802021-02-21 12:13:19.000001AgentAnsweredMatt TashjianSEO151568.00.042.0303015
6138812021-02-21 12:13:54.000002AgentAnsweredMatthew DuffSEO18692.00.02.024246
6138822021-02-21 12:14:41.999997AgentAnsweredLauren BaschSEO17540.00.00.022225
6138832021-02-21 12:17:38.999996AgentAnsweredMatt TashjianSEO1414121.00.02.0282814
6138842021-02-21 12:18:55.000003AgentAnsweredMeredith ChesneySEO44289.00.01.0884
6138852021-02-21 12:20:36.000004AbandonedNaNSEO80NaNNaNNaN880
6138862021-02-21 12:22:32.999998AgentAnsweredMatt TashjianSEO171755.00.012.0343417
6138872021-02-21 12:24:08.999997AgentAnsweredJunior FetchetSEO77201.00.042.014147